The overall project goals are to study the cortical network dynamics of human upper limb motor control spanning two distinct spatial scales recorded with electrocorticography (ECoG), and to demonstrate that these dynamics can be estimated in real-time and used to control the JHU Applied Physics Lab Modular Prosthetic Limb (MPL) during execution of functionally useful complex action sequences. Our human subjects will be instructed to perform complete functional movements characteristic of activities of daily living. We will analyze the task-related temporal evolution in the strength and pattern o interactions among large-scale cortical networks known to be recruited in visually-guided reach-to-grasp tasks. Using multi-scale subdural ECoG with combinations of routine clinical macro-electrodes (2.3 mm diameter, 1 cm spacing) recording activity of broadly spread elements/nodes of neural networks, and inset arrays of microelectrodes (75 ?m diameter, 0.9 mm spacing) recording the activity of local sub-networks, we will test our overall hypothesis that there is a functional hierarchy between the two scales (Aim 1). More specifically, we hypothesize that large-scale network dynamics involving premotor/motor cortex reflect the evolution of sensory-motor processing demands during complex action sequences, while micro-scale population activity and network dynamics in motor cortex reflect the low-level kinematics of these tasks. We will utilize methods of estimating dynamic effective connectivity developed by our team to study interactions between these scales and test whether there exists a spatially heterogeneous and hierarchical structure within the macro-micro scale networks. The results of these analyses have wide-ranging clinical implications for both the optimal scale of functional mapping for clinical diagnostic purposes and the extent of implantations for neuroprosthetic control. We will exploit multi-scale ECoG recordings and online estimates of the dynamics of neural activation and large-scale/local network interactions to achieve control of the MPL during functionally useful tasks (Aim 2). This approach will go beyond traditional paradigms that have developed neural control over individual degrees of freedom. We will do this by embedding low-level control within an innovative framework whereby knowledge of task goals supplement direct kinematic decoding. This project will build on our team's previous successes in implementing a system for semi-autonomous ECoG control of the MPL, employing machine vision and route-planning algorithms, during complex interactions with objects requiring the coordination of multiple joints. This system will be able to leverage for the first time the rich complexity of temporally and spatially resolved network dynamics correlated with high-level goals to achieve functionally useful control of an advanced neuroprosthetic limb.